(1) Field of Invention
The present invention relates to a controller adaptation system and, more specifically, to a controller adapter that gives a controller of a system (such as an autonomous and/or user controlled automobile or aircraft) the ability to quickly adapt to changes in the system's dynamics.
(2) Description of Related Art
Dynamic systems are typically operated via a controller. The controller is tasked with assessing operating parameters (such as the surrounding environment, etc.) and executing commands on the autonomous system to perform tasks and operate within the confines of the operating parameters.
Several systems and methods have been developed for controlling autonomous systems. For example, model-predictive control (see The List of Incorporated Literature References, Reference No. 1) computes optimal control commands to achieve a given control objective (e.g., keeping a car in the lane), but this computation relies on a forward model of the system dynamics and adapting this model to a sudden change requires another method.
Machine learning methods like support vector regression (see Literature Reference No. 2), Gaussian processes (see Literature Reference No. 3). and principal component analysis (see Literature Reference No. 4) typically require a lot of data to relearn a functional relationship. On-line learning methods gradually adapt an internal model with every single data point (see Literature Reference Nos. 5-7) but cannot adapt to a sudden change. The expected change in the system dynamics may be large, particularly, after damage. Due to such changes, on-line learning models are typically not sufficiently fast at adapting to sudden changes.
Thus, a continuing need exists for a fast controller adaptor that provides adaptation from sparse data due to sudden changes in system dynamics.